Overview

Dataset statistics

Number of variables23
Number of observations35829
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 MiB
Average record size in memory184.0 B

Variable types

Numeric9
Categorical10
Boolean4

Warnings

points_in_wallet is highly correlated with churn_risk_scoreHigh correlation
churn_risk_score is highly correlated with points_in_walletHigh correlation
used_special_discount is highly correlated with offer_application_preferenceHigh correlation
offer_application_preference is highly correlated with used_special_discountHigh correlation
points_in_wallet is highly correlated with churn_risk_scoreHigh correlation
feedback is highly correlated with churn_risk_scoreHigh correlation
membership_category is highly correlated with churn_risk_scoreHigh correlation
complaint_status is highly correlated with past_complaintHigh correlation
past_complaint is highly correlated with complaint_statusHigh correlation
churn_risk_score is highly correlated with points_in_wallet and 3 other fieldsHigh correlation
avg_transaction_value is highly correlated with churn_risk_scoreHigh correlation
complaint_status is highly correlated with past_complaintHigh correlation
used_special_discount is highly correlated with offer_application_preferenceHigh correlation
offer_application_preference is highly correlated with used_special_discountHigh correlation
past_complaint is highly correlated with complaint_statusHigh correlation
churn_risk_score is highly correlated with feedbackHigh correlation
feedback is highly correlated with churn_risk_scoreHigh correlation
df_index is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-06-27 21:05:50.465923
Analysis finished2021-06-27 21:06:13.940417
Duration23.47 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct35829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18498.27815
Minimum0
Maximum36991
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size280.0 KiB
2021-06-28T02:36:14.033470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1860.4
Q19254
median18482
Q327766
95-th percentile35134.6
Maximum36991
Range36991
Interquartile range (IQR)18512

Descriptive statistics

Standard deviation10676.32789
Coefficient of variation (CV)0.5771525222
Kurtosis-1.200629918
Mean18498.27815
Median Absolute Deviation (MAD)9256
Skewness0.0008948182059
Sum662774808
Variance113983977.3
MonotonicityStrictly increasing
2021-06-28T02:36:14.169990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
334451
 
< 0.1%
313861
 
< 0.1%
191001
 
< 0.1%
170531
 
< 0.1%
231981
 
< 0.1%
211511
 
< 0.1%
354921
 
< 0.1%
109281
 
< 0.1%
273201
 
< 0.1%
Other values (35819)35819
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
369911
< 0.1%
369901
< 0.1%
369891
< 0.1%
369881
< 0.1%
369871
< 0.1%
369861
< 0.1%
369851
< 0.1%
369841
< 0.1%
369831
< 0.1%
369821
< 0.1%

age
Real number (ℝ≥0)

Distinct55
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.12026571
Minimum10
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 KiB
2021-06-28T02:36:14.313606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123
median37
Q351
95-th percentile62
Maximum64
Range54
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.86535957
Coefficient of variation (CV)0.4274042566
Kurtosis-1.198183
Mean37.12026571
Median Absolute Deviation (MAD)14
Skewness-0.007266793459
Sum1329982
Variance251.7096342
MonotonicityNot monotonic
2021-06-28T02:36:14.459923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33698
 
1.9%
38692
 
1.9%
16692
 
1.9%
42690
 
1.9%
30688
 
1.9%
61686
 
1.9%
60681
 
1.9%
57678
 
1.9%
59674
 
1.9%
28673
 
1.9%
Other values (45)28977
80.9%
ValueCountFrequency (%)
10649
1.8%
11631
1.8%
12646
1.8%
13625
1.7%
14653
1.8%
15631
1.8%
16692
1.9%
17662
1.8%
18604
1.7%
19632
1.8%
ValueCountFrequency (%)
64652
1.8%
63634
1.8%
62658
1.8%
61686
1.9%
60681
1.9%
59674
1.9%
58661
1.8%
57678
1.9%
56662
1.8%
55669
1.9%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
F
17948 
M
17881 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F17948
50.1%
M17881
49.9%

Length

2021-06-28T02:36:14.720093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:14.792900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
f17948
50.1%
m17881
49.9%

Most occurring characters

ValueCountFrequency (%)
F17948
50.1%
M17881
49.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35829
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F17948
50.1%
M17881
49.9%

Most occurring scripts

ValueCountFrequency (%)
Latin35829
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F17948
50.1%
M17881
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII35829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F17948
50.1%
M17881
49.9%

region_category
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Town
18965 
City
12315 
Village
4549 

Length

Max length7
Median length4
Mean length4.380892573
Min length4

Characters and Unicode

Total characters156963
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVillage
2nd rowCity
3rd rowTown
4th rowCity
5th rowCity

Common Values

ValueCountFrequency (%)
Town18965
52.9%
City12315
34.4%
Village4549
 
12.7%

Length

2021-06-28T02:36:15.014908image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:15.094731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
town18965
52.9%
city12315
34.4%
village4549
 
12.7%

Most occurring characters

ValueCountFrequency (%)
T18965
12.1%
o18965
12.1%
w18965
12.1%
n18965
12.1%
i16864
10.7%
C12315
7.8%
t12315
7.8%
y12315
7.8%
l9098
5.8%
V4549
 
2.9%
Other values (3)13647
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter121134
77.2%
Uppercase Letter35829
 
22.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o18965
15.7%
w18965
15.7%
n18965
15.7%
i16864
13.9%
t12315
10.2%
y12315
10.2%
l9098
7.5%
a4549
 
3.8%
g4549
 
3.8%
e4549
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
T18965
52.9%
C12315
34.4%
V4549
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin156963
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T18965
12.1%
o18965
12.1%
w18965
12.1%
n18965
12.1%
i16864
10.7%
C12315
7.8%
t12315
7.8%
y12315
7.8%
l9098
5.8%
V4549
 
2.9%
Other values (3)13647
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII156963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T18965
12.1%
o18965
12.1%
w18965
12.1%
n18965
12.1%
i16864
10.7%
C12315
7.8%
t12315
7.8%
y12315
7.8%
l9098
5.8%
V4549
 
2.9%
Other values (3)13647
8.7%

membership_category
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Basic Membership
7473 
No Membership
7466 
Gold Membership
6574 
Silver Membership
5806 
Premium Membership
4308 

Length

Max length19
Median length16
Mean length15.94574228
Min length13

Characters and Unicode

Total characters571320
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlatinum Membership
2nd rowPremium Membership
3rd rowNo Membership
4th rowNo Membership
5th rowNo Membership

Common Values

ValueCountFrequency (%)
Basic Membership7473
20.9%
No Membership7466
20.8%
Gold Membership6574
18.3%
Silver Membership5806
16.2%
Premium Membership4308
12.0%
Platinum Membership4202
11.7%

Length

2021-06-28T02:36:15.332412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:15.423171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
membership35829
50.0%
basic7473
 
10.4%
no7466
 
10.4%
gold6574
 
9.2%
silver5806
 
8.1%
premium4308
 
6.0%
platinum4202
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e81772
14.3%
i57618
10.1%
m48647
8.5%
r45943
 
8.0%
s43302
 
7.6%
35829
 
6.3%
M35829
 
6.3%
b35829
 
6.3%
h35829
 
6.3%
p35829
 
6.3%
Other values (14)114893
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter463833
81.2%
Uppercase Letter71658
 
12.5%
Space Separator35829
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e81772
17.6%
i57618
12.4%
m48647
10.5%
r45943
9.9%
s43302
9.3%
b35829
7.7%
h35829
7.7%
p35829
7.7%
l16582
 
3.6%
o14040
 
3.0%
Other values (7)48442
10.4%
Uppercase Letter
ValueCountFrequency (%)
M35829
50.0%
P8510
 
11.9%
B7473
 
10.4%
N7466
 
10.4%
G6574
 
9.2%
S5806
 
8.1%
Space Separator
ValueCountFrequency (%)
35829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin535491
93.7%
Common35829
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e81772
15.3%
i57618
10.8%
m48647
9.1%
r45943
8.6%
s43302
8.1%
M35829
 
6.7%
b35829
 
6.7%
h35829
 
6.7%
p35829
 
6.7%
l16582
 
3.1%
Other values (13)98311
18.4%
Common
ValueCountFrequency (%)
35829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII571320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e81772
14.3%
i57618
10.1%
m48647
8.5%
r45943
 
8.0%
s43302
 
7.6%
35829
 
6.3%
M35829
 
6.3%
b35829
 
6.3%
h35829
 
6.3%
p35829
 
6.3%
Other values (14)114893
20.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
False
20646 
True
15183 
ValueCountFrequency (%)
False20646
57.6%
True15183
42.4%
2021-06-28T02:36:15.520911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Gift Vouchers/Coupons
12253 
Credit/Debit Card Offers
11860 
Without Offers
11716 

Length

Max length24
Median length21
Mean length19.70406654
Min length14

Characters and Unicode

Total characters705977
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGift Vouchers/Coupons
2nd rowGift Vouchers/Coupons
3rd rowGift Vouchers/Coupons
4th rowGift Vouchers/Coupons
5th rowCredit/Debit Card Offers

Common Values

ValueCountFrequency (%)
Gift Vouchers/Coupons12253
34.2%
Credit/Debit Card Offers11860
33.1%
Without Offers11716
32.7%

Length

2021-06-28T02:36:15.888692image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:15.963494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
offers23576
28.2%
vouchers/coupons12253
14.7%
gift12253
14.7%
credit/debit11860
14.2%
card11860
14.2%
without11716
14.0%

Most occurring characters

ValueCountFrequency (%)
e59549
 
8.4%
r59549
 
8.4%
f59405
 
8.4%
t59405
 
8.4%
o48475
 
6.9%
s48082
 
6.8%
i47689
 
6.8%
47689
 
6.8%
u36222
 
5.1%
C35973
 
5.1%
Other values (13)203939
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter526544
74.6%
Uppercase Letter107631
 
15.2%
Space Separator47689
 
6.8%
Other Punctuation24113
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59549
11.3%
r59549
11.3%
f59405
11.3%
t59405
11.3%
o48475
9.2%
s48082
9.1%
i47689
9.1%
u36222
6.9%
h23969
 
4.6%
d23720
 
4.5%
Other values (5)60479
11.5%
Uppercase Letter
ValueCountFrequency (%)
C35973
33.4%
O23576
21.9%
G12253
 
11.4%
V12253
 
11.4%
D11860
 
11.0%
W11716
 
10.9%
Space Separator
ValueCountFrequency (%)
47689
100.0%
Other Punctuation
ValueCountFrequency (%)
/24113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin634175
89.8%
Common71802
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59549
 
9.4%
r59549
 
9.4%
f59405
 
9.4%
t59405
 
9.4%
o48475
 
7.6%
s48082
 
7.6%
i47689
 
7.5%
u36222
 
5.7%
C35973
 
5.7%
h23969
 
3.8%
Other values (11)155857
24.6%
Common
ValueCountFrequency (%)
47689
66.4%
/24113
33.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII705977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59549
 
8.4%
r59549
 
8.4%
f59405
 
8.4%
t59405
 
8.4%
o48475
 
6.9%
s48082
 
6.8%
i47689
 
6.8%
47689
 
6.8%
u36222
 
5.1%
C35973
 
5.1%
Other values (13)203939
28.9%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Desktop
18706 
Smartphone
13444 
Both
3679 

Length

Max length10
Median length7
Mean length7.81763376
Min length4

Characters and Unicode

Total characters280098
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDesktop
2nd rowDesktop
3rd rowDesktop
4th rowDesktop
5th rowSmartphone

Common Values

ValueCountFrequency (%)
Desktop18706
52.2%
Smartphone13444
37.5%
Both3679
 
10.3%

Length

2021-06-28T02:36:16.194960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:16.277738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
desktop18706
52.2%
smartphone13444
37.5%
both3679
 
10.3%

Most occurring characters

ValueCountFrequency (%)
t35829
12.8%
o35829
12.8%
e32150
11.5%
p32150
11.5%
D18706
6.7%
s18706
6.7%
k18706
6.7%
h17123
 
6.1%
S13444
 
4.8%
m13444
 
4.8%
Other values (4)44011
15.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter244269
87.2%
Uppercase Letter35829
 
12.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t35829
14.7%
o35829
14.7%
e32150
13.2%
p32150
13.2%
s18706
7.7%
k18706
7.7%
h17123
7.0%
m13444
 
5.5%
a13444
 
5.5%
r13444
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
D18706
52.2%
S13444
37.5%
B3679
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Latin280098
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t35829
12.8%
o35829
12.8%
e32150
11.5%
p32150
11.5%
D18706
6.7%
s18706
6.7%
k18706
6.7%
h17123
 
6.1%
S13444
 
4.8%
m13444
 
4.8%
Other values (4)44011
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII280098
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t35829
12.8%
o35829
12.8%
e32150
11.5%
p32150
11.5%
D18706
6.7%
s18706
6.7%
k18706
6.7%
h17123
 
6.1%
S13444
 
4.8%
m13444
 
4.8%
Other values (4)44011
15.7%

internet_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Wi-Fi
12016 
Mobile_Data
11953 
Fiber_Optic
11860 

Length

Max length11
Median length11
Mean length8.987775266
Min length5

Characters and Unicode

Total characters322023
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWi-Fi
2nd rowMobile_Data
3rd rowWi-Fi
4th rowMobile_Data
5th rowMobile_Data

Common Values

ValueCountFrequency (%)
Wi-Fi12016
33.5%
Mobile_Data11953
33.4%
Fiber_Optic11860
33.1%

Length

2021-06-28T02:36:16.508788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:16.589574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
wi-fi12016
33.5%
mobile_data11953
33.4%
fiber_optic11860
33.1%

Most occurring characters

ValueCountFrequency (%)
i59705
18.5%
a23906
 
7.4%
F23876
 
7.4%
b23813
 
7.4%
e23813
 
7.4%
_23813
 
7.4%
t23813
 
7.4%
W12016
 
3.7%
-12016
 
3.7%
M11953
 
3.7%
Other values (7)83299
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter214536
66.6%
Uppercase Letter71658
 
22.3%
Connector Punctuation23813
 
7.4%
Dash Punctuation12016
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i59705
27.8%
a23906
11.1%
b23813
 
11.1%
e23813
 
11.1%
t23813
 
11.1%
o11953
 
5.6%
l11953
 
5.6%
r11860
 
5.5%
p11860
 
5.5%
c11860
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
F23876
33.3%
W12016
16.8%
M11953
16.7%
D11953
16.7%
O11860
16.6%
Dash Punctuation
ValueCountFrequency (%)
-12016
100.0%
Connector Punctuation
ValueCountFrequency (%)
_23813
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin286194
88.9%
Common35829
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i59705
20.9%
a23906
8.4%
F23876
 
8.3%
b23813
 
8.3%
e23813
 
8.3%
t23813
 
8.3%
W12016
 
4.2%
M11953
 
4.2%
o11953
 
4.2%
l11953
 
4.2%
Other values (5)59393
20.8%
Common
ValueCountFrequency (%)
_23813
66.5%
-12016
33.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII322023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i59705
18.5%
a23906
 
7.4%
F23876
 
7.4%
b23813
 
7.4%
e23813
 
7.4%
_23813
 
7.4%
t23813
 
7.4%
W12016
 
3.7%
-12016
 
3.7%
M11953
 
3.7%
Other values (7)83299
25.9%

days_since_last_login
Real number (ℝ)

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-42.14390577
Minimum-999
Maximum26
Zeros0
Zeros (%)0.0%
Negative1944
Negative (%)5.4%
Memory size280.0 KiB
2021-06-28T02:36:16.691918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-999
5-th percentile-999
Q18
median12
Q316
95-th percentile22
Maximum26
Range1025
Interquartile range (IQR)8

Descriptive statistics

Standard deviation229.254856
Coefficient of variation (CV)-5.439810378
Kurtosis13.47173094
Mean-42.14390577
Median Absolute Deviation (MAD)4
Skewness-3.931931907
Sum-1509974
Variance52557.78899
MonotonicityNot monotonic
2021-06-28T02:36:16.818582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
122307
 
6.4%
132288
 
6.4%
142226
 
6.2%
152208
 
6.2%
112195
 
6.1%
102025
 
5.7%
162002
 
5.6%
-9991944
 
5.4%
91801
 
5.0%
171689
 
4.7%
Other values (17)15144
42.3%
ValueCountFrequency (%)
-9991944
5.4%
1323
 
0.9%
2590
 
1.6%
3830
2.3%
4967
2.7%
51190
3.3%
61222
3.4%
71406
3.9%
81529
4.3%
91801
5.0%
ValueCountFrequency (%)
2678
 
0.2%
25197
 
0.5%
24460
 
1.3%
23704
2.0%
22856
2.4%
21992
2.8%
201140
3.2%
191275
3.6%
181385
3.9%
171689
4.7%

avg_time_spent
Real number (ℝ)

Distinct25299
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.3738594
Minimum-2814.10911
Maximum3235.578521
Zeros0
Zeros (%)0.0%
Negative1659
Negative (%)4.6%
Memory size280.0 KiB
2021-06-28T02:36:16.952813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2814.10911
5-th percentile30.16
Q159.81
median161.62
Q3356.33
95-th percentile1029.421973
Maximum3235.578521
Range6049.687631
Interquartile range (IQR)296.52

Descriptive statistics

Standard deviation397.78465
Coefficient of variation (CV)1.634459226
Kurtosis5.002986165
Mean243.3738594
Median Absolute Deviation (MAD)123.02
Skewness0.5344852285
Sum8719842.01
Variance158232.6277
MonotonicityNot monotonic
2021-06-28T02:36:17.085460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.121
 
0.1%
34.7120
 
0.1%
33.6819
 
0.1%
34.3319
 
0.1%
32.9118
 
0.1%
31.4918
 
0.1%
33.2818
 
0.1%
33.7117
 
< 0.1%
33.9417
 
< 0.1%
32.9616
 
< 0.1%
Other values (25289)35646
99.5%
ValueCountFrequency (%)
-2814.109111
< 0.1%
-2281.2365261
< 0.1%
-2096.5806811
< 0.1%
-2093.1216061
< 0.1%
-2034.801881
< 0.1%
-2012.2673741
< 0.1%
-1960.4791691
< 0.1%
-1941.0354191
< 0.1%
-1918.4863391
< 0.1%
-1913.4051541
< 0.1%
ValueCountFrequency (%)
3235.5785211
< 0.1%
3040.411
< 0.1%
2899.661
< 0.1%
2861.231
< 0.1%
2770.561
< 0.1%
2747.891341
< 0.1%
2732.71
< 0.1%
2722.0777941
< 0.1%
2705.7566081
< 0.1%
2567.8256471
< 0.1%

avg_transaction_value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct35741
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29304.27231
Minimum800.46
Maximum99914.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 KiB
2021-06-28T02:36:17.230541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum800.46
5-th percentile3471.128
Q114194.65
median27584.53
Q340874.01
95-th percentile67690.73
Maximum99914.05
Range99113.59
Interquartile range (IQR)26679.36

Descriptive statistics

Standard deviation19484.56542
Coefficient of variation (CV)0.6649052813
Kurtosis1.427273826
Mean29304.27231
Median Absolute Deviation (MAD)13329.8
Skewness1.014141011
Sum1049942772
Variance379648289.6
MonotonicityNot monotonic
2021-06-28T02:36:17.372101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30126.022
 
< 0.1%
40490.432
 
< 0.1%
3432.732
 
< 0.1%
26705.42
 
< 0.1%
48790.612
 
< 0.1%
37388.332
 
< 0.1%
27913.32
 
< 0.1%
11573.442
 
< 0.1%
6970.82
 
< 0.1%
39623.12
 
< 0.1%
Other values (35731)35809
99.9%
ValueCountFrequency (%)
800.461
< 0.1%
804.341
< 0.1%
806.221
< 0.1%
806.711
< 0.1%
813.821
< 0.1%
815.221
< 0.1%
821.831
< 0.1%
822.71
< 0.1%
823.491
< 0.1%
823.681
< 0.1%
ValueCountFrequency (%)
99914.051
< 0.1%
99861.471
< 0.1%
99858.021
< 0.1%
99819.191
< 0.1%
99810.831
< 0.1%
99805.521
< 0.1%
99803.531
< 0.1%
99795.751
< 0.1%
99742.631
< 0.1%
99730.171
< 0.1%

avg_frequency_login_days
Real number (ℝ)

Distinct1587
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.96304027
Minimum-43.65270156
Maximum73.06199459
Zeros0
Zeros (%)0.0%
Negative659
Negative (%)1.8%
Memory size280.0 KiB
2021-06-28T02:36:17.508738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-43.65270156
5-th percentile5
Q110
median15.96304027
Q322
95-th percentile29
Maximum73.06199459
Range116.7146962
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.755654449
Coefficient of variation (CV)0.5484954184
Kurtosis2.461142555
Mean15.96304027
Median Absolute Deviation (MAD)5.963040266
Skewness0.006513732976
Sum571939.7697
Variance76.66148483
MonotonicityNot monotonic
2021-06-28T02:36:17.654871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.963040273419
 
9.5%
131361
 
3.8%
81326
 
3.7%
191319
 
3.7%
141315
 
3.7%
61304
 
3.6%
171303
 
3.6%
101296
 
3.6%
181292
 
3.6%
151291
 
3.6%
Other values (1577)20603
57.5%
ValueCountFrequency (%)
-43.652701561
< 0.1%
-43.625413461
< 0.1%
-37.424778141
< 0.1%
-35.815757631
< 0.1%
-34.417115271
< 0.1%
-33.499563311
< 0.1%
-28.592011521
< 0.1%
-27.900537791
< 0.1%
-25.485591071
< 0.1%
-25.366168061
< 0.1%
ValueCountFrequency (%)
73.061994591
< 0.1%
67.062421971
< 0.1%
63.523537361
< 0.1%
56.538478041
< 0.1%
56.161979241
< 0.1%
56.046586111
< 0.1%
55.652396471
< 0.1%
55.422852571
< 0.1%
55.376272291
< 0.1%
55.127855481
< 0.1%

points_in_wallet
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct23162
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.8492312
Minimum-760.6612363
Maximum2069.069761
Zeros0
Zeros (%)0.0%
Negative134
Negative (%)0.4%
Memory size280.0 KiB
2021-06-28T02:36:17.794532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-760.6612363
5-th percentile350.3458545
Q1624.31
median686.8492312
Q3757.09
95-th percentile1029.333759
Maximum2069.069761
Range2829.730997
Interquartile range (IQR)132.78

Descriptive statistics

Standard deviation185.2803719
Coefficient of variation (CV)0.2697540646
Kurtosis5.189221456
Mean686.8492312
Median Absolute Deviation (MAD)66.77923123
Skewness-0.09146186137
Sum24609121.11
Variance34328.81621
MonotonicityNot monotonic
2021-06-28T02:36:17.912181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
686.84923123341
 
9.3%
705.079
 
< 0.1%
780.928
 
< 0.1%
771.757
 
< 0.1%
760.547
 
< 0.1%
783.426
 
< 0.1%
719.786
 
< 0.1%
719.396
 
< 0.1%
734.46
 
< 0.1%
756.916
 
< 0.1%
Other values (23152)32427
90.5%
ValueCountFrequency (%)
-760.66123631
< 0.1%
-549.35749771
< 0.1%
-506.25671581
< 0.1%
-483.85640061
< 0.1%
-471.5770091
< 0.1%
-469.02039881
< 0.1%
-445.28845721
< 0.1%
-424.67052481
< 0.1%
-412.44168781
< 0.1%
-405.26703551
< 0.1%
ValueCountFrequency (%)
2069.0697611
< 0.1%
1816.9336961
< 0.1%
1780.7201731
< 0.1%
1763.3515941
< 0.1%
1759.0025321
< 0.1%
1755.4555121
< 0.1%
1755.0946931
< 0.1%
1751.3041951
< 0.1%
1750.6115621
< 0.1%
1736.3325941
< 0.1%

used_special_discount
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
True
19718 
False
16111 
ValueCountFrequency (%)
True19718
55.0%
False16111
45.0%
2021-06-28T02:36:18.005933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

offer_application_preference
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
True
19783 
False
16046 
ValueCountFrequency (%)
True19783
55.2%
False16046
44.8%
2021-06-28T02:36:18.052809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

past_complaint
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.1 KiB
False
18007 
True
17822 
ValueCountFrequency (%)
False18007
50.3%
True17822
49.7%
2021-06-28T02:36:18.102675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

complaint_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Not Applicable
18007 
Unsolved
4501 
Solved
4467 
Solved in Follow-up
4443 
No Information Available
4411 

Length

Max length24
Median length14
Mean length14.10000279
Min length6

Characters and Unicode

Total characters505189
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowSolved
3rd rowSolved in Follow-up
4th rowUnsolved
5th rowSolved

Common Values

ValueCountFrequency (%)
Not Applicable18007
50.3%
Unsolved4501
 
12.6%
Solved4467
 
12.5%
Solved in Follow-up4443
 
12.4%
No Information Available4411
 
12.3%

Length

2021-06-28T02:36:18.330496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:18.427237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
applicable18007
25.2%
not18007
25.2%
solved8910
12.5%
unsolved4501
 
6.3%
follow-up4443
 
6.2%
in4443
 
6.2%
no4411
 
6.2%
information4411
 
6.2%
available4411
 
6.2%

Most occurring characters

ValueCountFrequency (%)
l67133
13.3%
o53537
 
10.6%
p40457
 
8.0%
e35829
 
7.1%
35715
 
7.1%
i31272
 
6.2%
a31240
 
6.2%
N22418
 
4.4%
t22418
 
4.4%
A22418
 
4.4%
Other values (16)142752
28.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter397930
78.8%
Uppercase Letter67101
 
13.3%
Space Separator35715
 
7.1%
Dash Punctuation4443
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l67133
16.9%
o53537
13.5%
p40457
10.2%
e35829
9.0%
i31272
7.9%
a31240
7.9%
t22418
 
5.6%
b22418
 
5.6%
c18007
 
4.5%
v17822
 
4.5%
Other values (8)57797
14.5%
Uppercase Letter
ValueCountFrequency (%)
N22418
33.4%
A22418
33.4%
S8910
 
13.3%
U4501
 
6.7%
F4443
 
6.6%
I4411
 
6.6%
Space Separator
ValueCountFrequency (%)
35715
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4443
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin465031
92.1%
Common40158
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l67133
14.4%
o53537
11.5%
p40457
 
8.7%
e35829
 
7.7%
i31272
 
6.7%
a31240
 
6.7%
N22418
 
4.8%
t22418
 
4.8%
A22418
 
4.8%
b22418
 
4.8%
Other values (14)115891
24.9%
Common
ValueCountFrequency (%)
35715
88.9%
-4443
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII505189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l67133
13.3%
o53537
 
10.6%
p40457
 
8.0%
e35829
 
7.1%
35715
 
7.1%
i31272
 
6.2%
a31240
 
6.2%
N22418
 
4.4%
t22418
 
4.4%
A22418
 
4.4%
Other values (16)142752
28.3%

feedback
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
Poor Product Quality
6152 
Too many ads
6096 
No reason specified
6072 
Poor Website
6060 
Poor Customer Service
6056 
Other values (4)
5393 

Length

Max length24
Median length19
Mean length17.35560579
Min length12

Characters and Unicode

Total characters621834
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProducts always in Stock
2nd rowQuality Customer Care
3rd rowPoor Website
4th rowPoor Website
5th rowPoor Website

Common Values

ValueCountFrequency (%)
Poor Product Quality6152
17.2%
Too many ads6096
17.0%
No reason specified6072
16.9%
Poor Website6060
16.9%
Poor Customer Service6056
16.9%
Reasonable Price1382
 
3.9%
User Friendly Website1346
 
3.8%
Products always in Stock1345
 
3.8%
Quality Customer Care1320
 
3.7%

Length

2021-06-28T02:36:18.685546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:18.780293image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
poor18268
18.0%
quality7472
 
7.4%
website7406
 
7.3%
customer7376
 
7.3%
product6152
 
6.1%
too6096
 
6.0%
many6096
 
6.0%
ads6096
 
6.0%
no6072
 
6.0%
reason6072
 
6.0%
Other values (11)24284
24.0%

Most occurring characters

ValueCountFrequency (%)
o78472
12.6%
65561
 
10.5%
e60674
 
9.8%
r50663
 
8.1%
s38440
 
6.2%
i37151
 
6.0%
a32510
 
5.2%
t31096
 
5.0%
P27147
 
4.4%
c22352
 
3.6%
Other values (21)177768
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter481909
77.5%
Uppercase Letter74364
 
12.0%
Space Separator65561
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o78472
16.3%
e60674
12.6%
r50663
10.5%
s38440
8.0%
i37151
7.7%
a32510
 
6.7%
t31096
 
6.5%
c22352
 
4.6%
u22345
 
4.6%
d21011
 
4.4%
Other values (10)87195
18.1%
Uppercase Letter
ValueCountFrequency (%)
P27147
36.5%
C8696
 
11.7%
Q7472
 
10.0%
W7406
 
10.0%
S7401
 
10.0%
T6096
 
8.2%
N6072
 
8.2%
R1382
 
1.9%
U1346
 
1.8%
F1346
 
1.8%
Space Separator
ValueCountFrequency (%)
65561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin556273
89.5%
Common65561
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o78472
14.1%
e60674
 
10.9%
r50663
 
9.1%
s38440
 
6.9%
i37151
 
6.7%
a32510
 
5.8%
t31096
 
5.6%
P27147
 
4.9%
c22352
 
4.0%
u22345
 
4.0%
Other values (20)155423
27.9%
Common
ValueCountFrequency (%)
65561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII621834
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o78472
12.6%
65561
 
10.5%
e60674
 
9.8%
r50663
 
8.1%
s38440
 
6.2%
i37151
 
6.0%
a32510
 
5.2%
t31096
 
5.0%
P27147
 
4.4%
c22352
 
3.6%
Other values (21)177768
28.6%

churn_risk_score
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
3
10424 
4
10185 
5
9827 
2
2741 
1
2652 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35829
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

Length

2021-06-28T02:36:19.096863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:19.179642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

Most occurring characters

ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number35829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
Common35829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII35829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
310424
29.1%
410185
28.4%
59827
27.4%
22741
 
7.7%
12652
 
7.4%

joining_day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.68070557
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 KiB
2021-06-28T02:36:19.293681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.795512382
Coefficient of variation (CV)0.5609130495
Kurtosis-1.197211874
Mean15.68070557
Median Absolute Deviation (MAD)8
Skewness0.01515722792
Sum561824
Variance77.36103806
MonotonicityNot monotonic
2021-06-28T02:36:19.410371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
51241
 
3.5%
71234
 
3.4%
81219
 
3.4%
271204
 
3.4%
241204
 
3.4%
121202
 
3.4%
231197
 
3.3%
131195
 
3.3%
21195
 
3.3%
191195
 
3.3%
Other values (21)23743
66.3%
ValueCountFrequency (%)
11150
3.2%
21195
3.3%
31190
3.3%
41153
3.2%
51241
3.5%
61168
3.3%
71234
3.4%
81219
3.4%
91161
3.2%
101118
3.1%
ValueCountFrequency (%)
31672
1.9%
301072
3.0%
291089
3.0%
281162
3.2%
271204
3.4%
261137
3.2%
251145
3.2%
241204
3.4%
231197
3.3%
221192
3.3%

joining_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.53448324
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size280.0 KiB
2021-06-28T02:36:19.528089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.448567238
Coefficient of variation (CV)0.5277490372
Kurtosis-1.203034364
Mean6.53448324
Median Absolute Deviation (MAD)3
Skewness-0.01465061584
Sum234124
Variance11.892616
MonotonicityNot monotonic
2021-06-28T02:36:19.620693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
73088
8.6%
123085
8.6%
13053
8.5%
83048
8.5%
102991
8.3%
92988
8.3%
32979
8.3%
62977
8.3%
52971
8.3%
42968
8.3%
Other values (2)5681
15.9%
ValueCountFrequency (%)
13053
8.5%
22752
7.7%
32979
8.3%
42968
8.3%
52971
8.3%
62977
8.3%
73088
8.6%
83048
8.5%
92988
8.3%
102991
8.3%
ValueCountFrequency (%)
123085
8.6%
112929
8.2%
102991
8.3%
92988
8.3%
83048
8.5%
73088
8.6%
62977
8.3%
52971
8.3%
42968
8.3%
32979
8.3%

joining_year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size280.0 KiB
2017
12168 
2015
11906 
2016
11755 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters143316
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2016
4th row2016
5th row2017

Common Values

ValueCountFrequency (%)
201712168
34.0%
201511906
33.2%
201611755
32.8%

Length

2021-06-28T02:36:19.829138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-28T02:36:19.906932image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
201712168
34.0%
201511906
33.2%
201611755
32.8%

Most occurring characters

ValueCountFrequency (%)
235829
25.0%
035829
25.0%
135829
25.0%
712168
 
8.5%
511906
 
8.3%
611755
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number143316
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
235829
25.0%
035829
25.0%
135829
25.0%
712168
 
8.5%
511906
 
8.3%
611755
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
Common143316
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
235829
25.0%
035829
25.0%
135829
25.0%
712168
 
8.5%
511906
 
8.3%
611755
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII143316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
235829
25.0%
035829
25.0%
135829
25.0%
712168
 
8.5%
511906
 
8.3%
611755
 
8.2%

Interactions

2021-06-28T02:36:00.813430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.018878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.199399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.409833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.598330image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.799790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:01.967344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:02.268537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:02.424122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:02.576713image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:02.726314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:02.882895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.029502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.199050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.360617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.503238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.663809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:03.861278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.028833image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.240265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.416796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.599304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.769849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:04.938398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.088996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.262532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.421107image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.562730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.707342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:05.896865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.051422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.206010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.350654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.487257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.624212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.754906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:06.881556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.015210image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.141872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.270485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.399184image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.527353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.655609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.784361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:07.919044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.042847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.190943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.320095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.443918image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.574612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.697723image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:08.823523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.084259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.211930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.335646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.464299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.583016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.707195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.838887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:09.962652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.088305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.218924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.351930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.480706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.631303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.767937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:10.895630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.026566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.167627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.297104image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.425382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.559025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.689809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.823483image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:11.945427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.067731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.201813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.324634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.445250image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.567922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-06-28T02:36:12.700003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-06-28T02:36:20.003673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-28T02:36:20.227074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-28T02:36:20.458490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-28T02:36:20.715812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-28T02:36:21.086777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-28T02:36:12.976219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-28T02:36:13.652231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexagegenderregion_categorymembership_categoryjoined_through_referralpreferred_offer_typesmedium_of_operationinternet_optiondays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_scorejoining_dayjoining_monthjoining_year
0018FVillagePlatinum MembershipNoGift Vouchers/CouponsDesktopWi-Fi17300.6353005.2517.0781.750000YesYesNoNot ApplicableProducts always in Stock21782017
1132FCityPremium MembershipNoGift Vouchers/CouponsDesktopMobile_Data16306.3412838.3810.0686.849231YesNoYesSolvedQuality Customer Care12882017
2244FTownNo MembershipYesGift Vouchers/CouponsDesktopWi-Fi14516.1621027.0022.0500.690000NoYesYesSolved in Follow-upPoor Website511112016
3337MCityNo MembershipYesGift Vouchers/CouponsDesktopMobile_Data1153.2725239.566.0567.660000NoYesYesUnsolvedPoor Website529102016
4431FCityNo MembershipNoCredit/Debit Card OffersSmartphoneMobile_Data20113.1324483.6616.0663.060000NoYesYesSolvedPoor Website51292017
5513MCityGold MembershipNoGift Vouchers/CouponsDesktopWi-Fi23433.6213884.7724.0722.270000YesNoYesUnsolvedNo reason specified3812016
6621MTownGold MembershipYesGift Vouchers/CouponsDesktopMobile_Data1055.388982.5028.0756.210000YesNoYesSolved in Follow-upNo reason specified31932015
7742MTownNo MembershipNoCredit/Debit Card OffersBothFiber_Optic19429.1144554.8224.0568.080000NoYesYesUnsolvedPoor Product Quality51272016
8844MVillageSilver MembershipNoWithout OffersSmartphoneFiber_Optic15191.0718362.3120.0686.849231YesNoYesSolved in Follow-upPoor Customer Service314122016
9945FTownNo MembershipNoGift Vouchers/CouponsDesktopWi-Fi1097.3119244.1628.0706.230000NoYesYesNo Information AvailablePoor Customer Service430112016

Last rows

df_indexagegenderregion_categorymembership_categoryjoined_through_referralpreferred_offer_typesmedium_of_operationinternet_optiondays_since_last_loginavg_time_spentavg_transaction_valueavg_frequency_login_dayspoints_in_walletused_special_discountoffer_application_preferencepast_complaintcomplaint_statusfeedbackchurn_risk_scorejoining_dayjoining_monthjoining_year
358193698245FTownPremium MembershipNoGift Vouchers/CouponsDesktopWi-Fi1034.93000041558.9319.00000703.030000YesNoNoNot ApplicablePoor Product Quality33182016
358203698345MTownBasic MembershipYesWithout OffersSmartphoneWi-Fi949.33000045358.4911.00000242.979625YesNoNoNot ApplicablePoor Customer Service53082016
358213698451MTownGold MembershipNoWithout OffersDesktopFiber_Optic24312.33000063446.712.00000778.700000NoYesNoNot ApplicableProducts always in Stock17102016
358223698512FVillagePremium MembershipNoGift Vouchers/CouponsDesktopFiber_Optic13418.38000056397.217.00000725.890000YesYesYesUnsolvedProducts always in Stock225102016
358233698627MTownPlatinum MembershipYesCredit/Debit Card OffersDesktopMobile_Data13135.8300008225.6816.00000748.570000YesNoNoNot ApplicableNo reason specified3792015
358243698746FTownBasic MembershipNoCredit/Debit Card OffersDesktopWi-Fi2-650.68275927277.686.00000639.510000NoYesYesNo Information AvailableNo reason specified42192017
358253698829FTownBasic MembershipNoWithout OffersSmartphoneWi-Fi13-638.12342111069.7128.00000527.990000YesNoNoNot ApplicablePoor Customer Service52762016
358263698923FTownBasic MembershipYesGift Vouchers/CouponsDesktopWi-Fi12154.94000038127.5615.96304680.470000NoYesYesUnsolvedPoor Website41192016
358273699053MVillagePlatinum MembershipNoGift Vouchers/CouponsSmartphoneMobile_Data15482.6100002378.8620.00000197.264414YesYesNoNot ApplicableNo reason specified31562017
358283699135MTownSilver MembershipNoGift Vouchers/CouponsDesktopMobile_Data1579.1800002189.6815.96304719.970000YesNoNoNot ApplicableQuality Customer Care223102015